Method and devices for optimizing backup power control using machine learning

ABSTRACT

Methods and devices optimize backup power management for network devices connected to backup power sources including two different batteries. Machine learning techniques are used to predict upcoming power outages affecting the network device based on power-related historic information and current conditions. A backup-power operation plan prescribing usage of the backup power sources during the predicted power outages is then generated. The backup-power operation plan schedules a battery among the backup power devices to be used at least twice without being recharged.

TECHNICAL FIELD

Embodiments of the subject matter disclosed herein relate to usingmachine learning (ML) techniques to develop a backup-power operationplan for using different backup sources to power a network device incase of electrical grid outages, so as to minimize recharging costs andthe number of discharging/recharging cycles.

BACKGROUND

A network device (e.g., a radio base station, RBS, or all the hardwareat one site) in a radio communication network is typically powered bythe electric power grid and may have two or more different backupsources to be used if the grid becomes unavailable (i.e., power-gridoutages are called simply “outages” hereinafter). The term “networkdevice” encompasses all hardware that provides a core network'sfunctionality and services.

Batteries are widely used as backup sources, with lead-acid batteriesbeing the most common (and oldest) technology. Despite having arelatively low recharging efficiency of about 80% (i.e., only 80% of theenergy spent for recharging the battery is stored), lead-acid batteriesare inexpensive compared to batteries using newer technologies.Large-format lead-acid designs are frequently used for backup powersupplies in cellphone towers. The latest versions of lead-acid batteries(e.g., gel-cells and absorbed glass-matt batteries) improve storagetimes and reduce maintenance requirements.

Another type of battery frequently used for network devices are lithiumon batteries (using technology less than 30 years-old). This type ofbattery is more expensive per storage capacity, but more compact and has97% recharging efficiency.

Batteries are not the only type of backup sources used by networkdevices. Backup generators and non-conventional sources of energy mayalso be employed.

Conventionally, a power controller switches between backup sources basedon predefined rules. That is, when plural backup sources are availableto power the network device during an outage, switching from one sourceto another is triggered when and if certain conditions are met (e.g., athreshold is exceeded). For example, if a battery and a diesel generatorare the available backup sources, the available battery energydecreasing to 20% of its energy storage capacity triggers switching fromthe battery to the diesel generator.

FIG. 1 uses three related graphs (i.e., three graphs with the samehorizontal time axis) to illustrate conventional power control. The topgraph illustrates power supplied to the network device by two backupbatteries during four outage periods: T1, T2, T3 and T4. T1, T2 and T3last less than two hours each while T4 lasts more than four hours. Themiddle graph illustrates discharging (i.e., supplying the necessarypower to the network device is represented as positive) and charging ofthe first battery (when outage is over power grid supplies the chargingpower represented as negative). The bottom graph illustrates discharging(i.e., supplying the necessary power to the network device) and chargingof the second battery (the first and second battery being based ondifferent technologies). In this case, the first battery ispreferentially used (for example, because it is cheaper). Note thatenergy delivered is the product of power and time (the darker areas onthe graph). As soon as the grid becomes available, the first battery isrecharged. When, during the fourth power outage, T4, the first battery'savailable energy decreases below a predetermined threshold, the secondbattery starts being used.

This conventional approach is inefficient because it does not have theflexibility to take into consideration the battery's actual capability,which decreases over time. For example, a threshold of 20% of thebattery's nominal capacity for switching is appropriate only for“healthy” batteries (i.e., batteries operating according to theirnominal parameters). As the battery's capacity decreases, the 20%threshold becomes inappropriate.

Another disadvantage is that the conventional approach does not enablecost-efficient charging. In the fixed-rule approach illustrated in FIG.1 , batteries recharge as soon as the grid is back (i.e., the outage isover) although they may yet store substantial energy. On one hand, thecost of power may vary during the day, and, thus, recharging may occurat a peak time when the power price is high; therefore, recharging ismore expensive than it needs to be. On the other hand, recharging thebattery as soon as the grid is available as in the conventional approachtriggers multiple discharging/recharging cycles, which degrade thebattery faster, reducing the battery's “life” (i.e., period ofusability). In the situation illustrated in FIG. 1 , battery 1 wasrecharged four times. Frequent partial recharging is not necessary andshould be avoided, particularly since the batteries can sustain deeperdischarge before being recharged.

Conventional power control with predetermined rules and fixed thresholdsis thus inefficient when two or more backup sources are available. Inview of the above-identified problems, it has become desirable toimprove power control in order to overcome the conventional approach'sdrawbacks and lower the costs.

SUMMARY

In order to improve backup power management when two or more differentbackup sources are available, methods based on machine learning, ML, areused to predict power outages based on historic information and currentconditions. This prediction enables generating a backup-power operationplan which delays the recharging of a partially discharged battery usedas backup source until the grid power price is more cost-effective. Inother words, two or more portions of a battery's energy are used withoutrecharging the battery, with the battery being discharged deeper, aslong as it is foreseeable that the predicted backup power needs can bemet by still-available backup capacity, which includes remaining energyin the partially-discharged battery.

According to an embodiment, there is a method for optimizing usage ofthe backup power supply of a network device in a radio communicationnetwork. The method includes predicting upcoming power outages usingmachine learning techniques, based on power-related historic informationand current conditions. The method then includes generating abackup-power operation plan prescribing usage of backup power sourcesincluding two different batteries connected and configured to supplypower to the network device during the predicted power outages. Thebackup-power operation plan schedules at least one of the batteriesamong the power sources to be used twice without being recharged. Thenetwork device receives backup power during outages according to thebackup-power operation plan.

According to another embodiment, there is a network device including acommunication interface and a data processing unit. The data processingunit is configured to perform at least one of (1) predicting upcomingpower outages affecting the network device based on power-relatedhistoric information and current conditions, and (2) generating abackup-power operation plan prescribing usage of backup power sourcesincluding two different batteries connected and configured to supplypower to the network device during the upcoming power outages. The dataprocessing unit is also configured to control provision of poweraccording to the backup-power operation plan during outages. Thebackup-power operation plan schedules at least one of the batteriesamong the backup power sources to be used twice without being rechargedin-between.

According to yet another embodiment, there is a non-transitorycomputer-readable medium storing executable codes which, when executedby a computer, make the computer perform a method for optimizing usageof a backup power supply in a network device. The method includespredicting upcoming power outages using ML techniques, based onpower-related historic information and current conditions, andgenerating a backup-power operation plan prescribing usage of backuppower sources including two different batteries connected and configuredto supply power to the network device during the predicted poweroutages. The backup-power operation plan schedules at least one of thebatteries among the backup power devices to be used twice without beingrecharged in-between. The network device receives backup power duringoutages according to the backup-power operation plan.

According to yet another embodiment; there is a network device connectedto backup power sources including at least two different batteriesconnected and configured to supply power to the network device duringpower outages. The network device includes a first module configured topredict upcoming power outages using ML techniques, based onpower-related historic information and current conditions, a secondmodule configured to generate a backup-power operation plan prescribingusage of the backup power sources during the predicted power outages,and a third module configured to monitor and control the backup powersources to provide backup power according to the backup-power plan whenoutages occur. Here also, the backup-power operation plan schedules atleast one of the batteries among the backup power devices to be usedtwice without being recharged in-between.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate one or more embodiments and,together with the description, explain these embodiments. In thedrawings:

FIG. 1 illustrates backup power control according to the conventionalapproach;

FIG. 2 is a flowchart of a backup power control method according to anembodiment;

FIG. 3 is a graph of probability versus duration usable in modelingoutages according to an embodiment;

FIG. 4 illustrates backup power control according to an embodiment;

FIG. 5 illustrates backup power management according to an embodiment;

FIG. 6 illustrates a network device according to an embodiment;

FIG. 7 is a flowchart of a method optimizing usage of a backup powersupply according to an embodiment;

FIG. 8 is a flowchart of another method optimizing usage of a backuppower supply according to an embodiment; and

FIG. 9 is an apparatus optimizing usage of a backup power supplyaccording to an embodiment.

DETAILED DESCRIPTION

The following description of the embodiments refers to the accompanyingdrawings. The same reference numbers in different drawings identify thesame or similar elements. The following detailed description does notlimit the invention. Instead, the scope of the invention is defined bythe appended claims. The embodiments are related to backup power controland are described in the context of a radio communication network.

Reference throughout the specification to “one embodiment” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with an embodiment is included in at least oneembodiment of the subject matter disclosed. Thus, the appearance of thephrases “in one embodiment” or “in an embodiment” in various placesthroughout the specification is not necessarily referring to the sameembodiment. Further, particular features, structures or functions may becombined in any suitable manner in one or more embodiments.

Various embodiments described in this section optimize backup poweroperations when two or more backup sources including different batteriesare connected to a network device. Machine learning, ML, techniques areused to predict upcoming outages. At least one battery among the backupsources is used more than once without recharging so as to minimizerecharging cost and the number of discharging/recharging cycles.

FIG. 2 is a flowchart of a method 200 for optimizing usage of a backuppower supply of a network device in a radio communication networkaccording to an embodiment. Method 200 includes predicting upcomingpower outages using ML techniques, based on power-related historicinformation and current conditions at 210.

The power-related historic information may include:

1) date, time of day and duration of past power outages,

2) the evolution of the network device's load (i.e., the networkdevice's power level as a function of time), and

3) previous outage prediction performance for similar conditions.

The current conditions may include:

1) P=power currently used (i.e., current load);

2) tday=time of day (as outages are more likely during peak hours);

3) tweek=day of the week (as grid reliability may differ on weekendsversus work days);

4) tyear=date, i.e., time of year (as outages can relate to season);

5) current weather; and

6) weather prediction (which may be obtained using some network weatherservice).

Various ML techniques may be employed to predict future power outagesand their duration. ML algorithms such as LSTM (Long Short-Term Memory)and deep neural networks may be used.

Another ML technique that may be used is learning a classification modelfor the probability of a power outage during a future time interval(e.g., the next four hours). This ML technique may use as input a numberand duration of outages that have occurred during a time intervalsimilar to the future time interval and similar conditions. The outputof this ML technique is a prediction of one (or more) upcoming poweroutage(s) during the future interval, with a specific probability (e.g.,a 95% probability to have one outage during the next four hours). Asimilar approach may be used for predicting likelihood (i.e., aprobability) of no outage occurring in the future time interval.

Regression is another example of an ML technique that may be employedfor predicting upcoming outages. Regression predicts time (andoptionally duration) of an upcoming outage based on the same input asthe classification. The regression's output may include a confidenceinterval around the prediction.

The power outage occurrence may also be modeled as an arrival process.The arrival process is a form of stochastic process with random eventsthat follow, as an assembly, a certain distribution. For example, ifmodeled according to a Poisson distribution, the time intervals betweenpower outages are exponentially distributed. Other distributions of thetime intervals may be used. Modeling may output an expected time of thenext outage within some bounds.

In addition to the expected times of upcoming outages, duration of theoutages may also be modeled according to a probability distribution asillustrated by the probability versus duration graph in FIG. 3 . Curve310 corresponds to a network device for which long-lasting power outagesare very likely, curve 320 corresponds to another network device forwhich long-lasting power outages are moderately likely, and curve 330corresponds to yet another network device for which long-lasting poweroutages are unlikely.

Returning now to FIG. 2 , method 200 further includes generating abackup-power operation plan prescribing usage of backup power sourcesincluding two different batteries connected and configured to supplypower to the network device during the predicted power outages at S220.By “different” batteries here it should be understood different type oftechnologies such as a lead-acid battery and a lithium-ion battery. Thebackup-power operation plan schedules at least one of the batteries tobe used twice or more times without being recharged between uses. Thatis, the battery is used in portions/packages. The outage predictionenables assertion that the network device's future power needs can bemet by the energy available in the backup power sources even if thebattery has been partially discharged.

Unlike the conventional method in which a battery was recharged as soonas grid power became available, in view of the prediction, therecharging may be delayed until the discharge becomes deeper, thusminimizing the number of discharging/recharging cycles. In other words,repeated use of the battery without recharging is planned as if thebattery capacity is split into packages to be used one at a time duringan outage. Delaying the recharging also allows scheduling this operationwhen the grid power price is low. A battery power counter may be used tomonitor battery charging state.

The backup-power operation plan may use current information about thebackup power sources, including one or more of power, maximum storedenergy and recharging efficiency for each of the batteries. Thisapproach has the flexibility of taking into consideration the actual(and not nominal) backup sources' parameters, flexibility which wasmissing in the conventional approach.

Thus, the backup-power operation plan includes not only instructionsrelated to the backup power sources used during power outages (which oneand for how long), but also battery recharging instructions. Theserecharging instructions indicate recommended time and estimated durationof recharging for each of the batteries and are generated based on, forexample: individual recharging efficiency (e.g., 80% for lead-acidbatteries and 97% for lithium-ion batteries), predicted gridavailability, cost of the grid power, predicted power outages, etc.

FIG. 4 uses three related graphs similar to the ones in FIG. 1 toillustrate backup power control according to an embodiment. The topgraph is the same as the one in FIG. 1 , showing the power supplied bythe two batteries to the network device during four outage periods:T1-T4 (with T1, T2 and T3 lasting less than two hours each and T4lasting more than four hours). The middle graph illustrates dischargingand charging of the first battery, and the bottom graph illustratesdischarging and charging of the second battery. In this case, the firstbattery is partially used during all four outage periods, for example,until its energy storage capacity decreases below a predeterminedthreshold (e.g., a predefined percentage of its energy storagecapacity). The first battery is thus used plural times without beingcharged. During the fourth power outage, T4, the second battery is usedas a backup source for the last portion of the fourth outage. Unlike theconventional approach, the first battery is here recharged only onetime, and it is not recharged using the second battery's power.

FIG. 5 illustrates backup power management according to an embodiment.The network device (labeled RBS) prompts the power controller, PC, toprovide a backup-power operation plan at S510. The power controller maybe collocated with the network device or at another location. The powercontroller predicts upcoming outages and generates the backup-poweroperation plan at S520. Then, at S530, the power controller sends theplan to the network device. The plan includes instructions such as: usethe first battery, BT1, at starting time t1, and switch to using thesecond battery, BT2, at time t2. Thus, when an outage occurs after t1but before t2, the network device uses the first battery as a backuppower source at S540, and, if an outage occurs after t2, the networkdevice uses the second battery as a backup power source at S550.

The methods according to various embodiments described in this sectionmay be executed periodically and/or “on demand.” For example, the methodmay be performed every day and generate a backup-power operation planfor the following 24 hours or it may refer to peak hours only.Alternatively or additionally, certain events may trigger executing themethod. For example, the method may be executed if the weather forecastindicates conditions making outages more likely, or if instability ofthe electric grid is observed. Even if there is a backup-power operationplan spanning a current time period, if the backup power sourcemonitoring indicates departure from the expected stored energy exceedinga safe margin, the method may be reiterated to take into considerationsuch current conditions.

FIG. 6 illustrates a network device 600 according to an embodiment.Network device 600 is configured to execute backup power controlfunctionality of incorporated backup power sources 630 and 640, one ofwhich is a battery. Network device 600 has a network interface 610connecting it to the radio communication network 612, and a centralprocessing unit (CPU) 620 connected to interface 610 and including atleast one processor. CPU 620 is also connected to the backup powersources and to a data storage device 650.

CPU 620 is configured to predict upcoming power outages affecting thenetwork device based on power-related historic information and currentconditions, and to generate a backup-power operation plan prescribingusage of backup power sources 630 and 640. CPU 620 is also configured tocontrol the backup power sources to provide power during outages. Thebackup-power operation plan schedules a battery among the backup powersource to be used at least twice without being recharged in-between.

The CPU may retrieve the power-related historic information andinformation about the backup sources from data storage device 650 orfrom another location via network 612. Information related to thecurrent conditions may be obtained from sensors and counters or obtainedusing network services.

As already mentioned, predicting upcoming power outages and generating abackup-power operation plan may be performed at different locations inthe network. FIG. 7 is a flowchart of a method 700 performed by firsthardware that predicts the upcoming outages using machine learningtechniques, based on power-related historic information and currentconditions at S710 and causes second hardware to generate the batteryoperation plan for the upcoming power outages at S720. FIG. 8 is aflowchart of another method 800 performed by first hardware thattriggers second hardware to predict the upcoming power outages based onpower-related historic information and current conditions at S810 andgenerates the backup-power operation plan prescribing usage of at leasttwo different power sources connected and configured to supply power tothe network device during the predicted power outages at S820. The powersource is controlled according to the back-up power operation plan atS830. Either of first and second hardware may be collocated with thenetwork device and may control the backup sources to provide power (andrecharge) according to the backup-power operation plan.

According to yet another embodiment illustrated in FIG. 9 , a controller900 includes hardware and software of three modules 910, 920 and 930.First module, 910, is configured to predict upcoming power outages usingmachine learning techniques, based on power-related historic informationand current conditions. Second module, 920, is configured to generate abackup-power operation plan prescribing usage of at least two differentpower sources connected and configured to supply power to the networkdevice during the predicted power outages. Third module, 930, isconfigured to monitor and control the power sources to provide backuppower to the network device according to the backup-power plan.

The above-described embodiments have at least some of the followingadvantages. Predicting outages using machine learning techniques enablesefficient selection of backup sources to better match requirements withneeds. By deferring recharging a battery used as a backup power sourceand performing fewer discharging/charging cycles, battery life isextended. By delaying recharging until the price of grid power is lower,operation cost is reduced.

The embodiments disclosed in this section provide methods and networkdevices for optimizing backup power control for network devices withbackup power sources including different batteries. This description isnot intended to limit the invention. On the contrary, the exemplaryembodiments are intended to cover alternatives, modifications andequivalents, which are included in the scope of the invention. Further,in the detailed description of the exemplary embodiments, numerousspecific details are set forth in order to provide a comprehensiveunderstanding of the invention. However, one skilled in the art wouldunderstand that various embodiments may be practiced without suchspecific details.

Although the features and elements of the present exemplary embodimentsare described in the embodiments with particular combinations thereof,each feature or element can be used alone without the other features andelements of the embodiments or in various combinations with or withoutother features and elements disclosed herein. The methods or flowchartsprovided in the present application may be implemented in a computerprogram, software or firmware tangibly embodied in a computer-readablestorage medium for execution by a computer or a processor.

This written description uses examples of the subject matter disclosedto enable any person skilled in the art to practice the same, includingmaking and using any devices or systems and performing any incorporatedmethods. The patentable scope of the subject matter is defined by theclaims, and may include other examples that occur to those skilled inthe art. Such other examples are intended to be within the scope of theclaims.

What is claimed is:
 1. A method for optimizing usage of backup powersupply of a network device in a radio communication network, the methodcomprising: predicting upcoming power outages using machine learningtechniques, based on power-related historic information and currentconditions; and generating a backup-power operation plan based on thepredicted upcoming power outages, the backup-power operation planprescribing usage of backup power sources including two differentbatteries connected and configured to supply power to the network deviceduring the predicted power outages, wherein the predicting of theupcoming power outages includes modeling the upcoming power outages asan arrival process with a distribution, the backup-power operation plancomprises battery recharging instructions and schedules at least one ofthe batteries among the backup power sources to be used at least twicewithout being recharged, and the network device receives backup powerduring outages according to the backup-power operation plan that isbased on the predicted upcoming power outages.
 2. The method of claim 1,wherein the backup-power operation plan is generated using currentinformation about the backup sources including one or more of power,maximum stored energy and recharging efficiency for each of thebatteries.
 3. The method of claim 1, wherein the battery recharginginstructions comprises a recommended time and a duration of rechargingfor each of the batteries.
 4. The method of claim 1, wherein thepower-related historic information includes one or more of past outagetimes, dates and durations, network device's past power usage as afunction of time, and previous outage prediction performance for similarconditions.
 5. The method of claim 1, wherein the current conditionsinclude one or more of: current load, time of the day, day of the week,date, current weather, and predicted weather.
 6. The method of claim 1,wherein the backup-power operation plan spans a predetermined timeinterval and specifies order and duration of using each of the backuppower sources.
 7. The method of claim 1, wherein the battery recharginginstructions are determined so as to minimize both recharging cost basedon recharging power cost as function of time of day and a number ofdischarging/recharging cycles for the batteries.
 8. The method of claim1, wherein at least one of the predicting and the generating areexecuted by hardware in the radio communication network other than thenetwork device.
 9. The method of claim 1, wherein at least one piece ofinformation related to the current conditions is retrieved using anetwork service.
 10. A network device, including a communicationinterface and a data processing unit, wherein the network device isconfigured to perform at least one of predicting upcoming power outagesaffecting the network device based on power-related historic informationand current conditions; and generating a backup-power operation planbased on the predicted upcoming power outages, the backup-poweroperation plan prescribing usage of backup power sources including twodifferent batteries connected and configured to supply power to thenetwork device during the upcoming power outages, and to controlprovision of power during outages according to the backup-poweroperation plan that is based on the predicted upcoming power outages,wherein the predicting of the upcoming power outages includes modelingthe upcoming power outages as an arrival process with a distribution,the backup-power operation plan comprises battery recharginginstructions and schedules at least one of the batteries among thebackup power devices to be used at least twice without being recharged,and the network device receives backup power during outages according tothe backup-power operation plan that is based on the predicted upcomingpower outages.
 11. The network device of claim 10, wherein thebackup-power operation plan is generated using current information aboutthe backup power sources including one or more of power, maximum storedenergy and recharging efficiency for each of the batteries.
 12. Thenetwork device of claim 10, wherein the battery recharging instructionscomprises a recommended time and a duration of recharging for each ofthe batteries.
 13. The network device of claim 10, wherein thepower-related historic information includes one or more of past outagetimes, dates and durations, network device's past power usage as afunction of time, and previous outage prediction performance for similarconditions.
 14. The network device of claim 10, wherein the currentconditions include one or more of: current load, time of the day, day ofthe week, date, current weather, and predicted weather.
 15. The networkdevice of claim 10, wherein the backup-power operation plan spans apredetermined time interval and specifies order and duration of usingeach of the backup power sources.
 16. The network device of claim 10,wherein the recharging instructions are determined so as to minimizeboth recharging cost based on recharging power cost as function of timeof day and a number of discharging/recharging cycles for the batteries.17. The network device of claim 10, wherein one of the predicting andthe generating are executed by hardware in the radio communicationnetwork outside the network device.
 18. The network device of claim 10,wherein at least one piece of information related to the currentconditions is retrieved using a network service.
 19. A non-transitorycomputer readable medium storing executable codes which, when executedby a computer make the computer perform a method for optimizing usage ofbackup power supply in a network device, the method comprising:predicting upcoming power outages using machine learning techniques,based on power-related historic information and current conditions; andgenerating a backup-power operation plan based on the predicted upcomingpower outages, the backup-power operation plan prescribing usage ofbackup power sources including two different batteries connected andconfigured to supply power to the network device during the predictedpower outages, wherein the predicting of the upcoming power outagesincludes modeling the upcoming power outages as an arrival process witha distribution, the backup-power operation plan comprises batteryrecharging instructions and schedules at least one of the batteriesamong the backup power devices to be used at least twice without beingrecharged, and the network device receives backup power during outagesaccording to the backup-power operation plan that is based on thepredicted upcoming power outages.
 20. The method of claim 1, wherein thedistribution is a Poisson distribution.
 21. The method of claim 1,wherein the arrival process is a form of stochastic process.
 22. Themethod of claim 1, wherein the different batteries are implemented withtwo different types of battery technologies.
 23. The network device ofclaim 10, wherein the distribution is a Poisson distribution.
 24. Thenetwork device of claim 10, wherein the arrival process is a form ofstochastic process.
 25. The network device of claim 10, wherein thedifferent batteries are implemented with two different types of batterytechnologies.